Beamforming is a technique which extracts the desired signal contaminated by interference based on directivity, i.e., spatial signal selectivity. This extraction is performed by processing the signals obtained by multiple sensors such as microphones located at different positions in the space. The principle of beamforming has been known for a long time. Because of the vast amount of necessary signal processing, most research and development effort has been focused on geological investigations and sonar, which can afford a high cost. With the advent of LSI technology, the required amount of signal processing has become relatively small. As a result, a variety of research projects where acoustic beamforming is applied to consumer-oriented applications such as cellular phone speech enhancement, have been carried out. Microphone array could contain multiple microphones; for the simplicity, two microphones array system is widely used.
Applications of beamforming include microphone arrays for speech enhancement. The goal of speech enhancement is to remove undesirable signals such as noise and reverberation. Amount research areas in the field of speech enhancement are teleconferencing, hands-free telephones, hearing aids, speech recognition, intelligibility improvement, and acoustic measurement.
Beamforming can be considered as multi-dimensional signal processing in space and time. Ideal conditions assumed in most theoretical discussions are not always maintained. The target DOA (direction of arrival), which is assumed to be stable, does change with the movement of the speaker. The sensor gains, which are assumed uniform, exhibit significant distribution. As a result, the performance obtained by beamforming may not be as good as expected. Steering vector errors are inevitable because the propagation model does not always reflect the non-stationary physical environment. The steering vector is sensitive to errors in the microphone positions, those in the microphone characteristics, and those in the assumed target DOA (which is also known as the look direction). For teleconferencing and hands-free communication, the error in the assumed target DOA is the dominant factor. Therefore, robustness against steering-vector errors caused by these array imperfections are become more and more important.
A beamformer which adaptively forms its directivity pattern is called an adaptive beamformer. It simultaneously performs beam steering and null steering. In most traditional acoustic beamformers, however, only null steering is performed with an assumption that the target DOA is known a priori. Due to adaptive processing, deep nulls can be developed. Adaptive beamformers naturally exhibit higher interference suppression capability than its fixed counterpart which may be called fixed beamformer.
The traditional adaptive beamformer/noise cancellation suffers from target speech signal cancellation due to steering vector errors, which is caused by an undesirable phase difference between two microphones input signals for the target. This is specially true when the target source or the microphone array is moving in space. Even if the phase between two microphones input signals is aligned, the output target signal from a fixed beamformer could still possibly have lower SNR (target signal to noise ratio) than the best one of the microphone array component signals; this means that one of the microphones could possibly receive higher SNR than the output target signal from a fixed beamformer. A phase error leads to target signal leakage, which results in target signal cancellation at the output. Adaptive filter technology is a widely used to adaptively and precisely align the target signals from different microphones; correctly controlling a step size of the adaptive filter is the key to have a robust performance.